In the first phase, we train the model on the labeled data from the first 5 and 80 classes from CIFAR-10 and CIFAR-100 datasets respectively for 200 epochs. In the next phase, classes are identified from the unlabeled data guided by the Spacing Loss. tab:twostage showcases the results. Our method consistently outperforms existing methods by a large margin, showing the efficacy of the proposed Spacing Loss. RS [1]} and NCL [2]} are adapted to the two-stage setting for fair comparison (denoted by \(^*\) ).
